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Traffic Sign Identification. Team G Project 15. Team members. Lajos Rodek - Szeged, Hungary Marcin Rogucki - Lodz, Poland Mircea Nanu   - Timisoara, Romania     Selman Kulac - Ankara, Turkey     Zsolt Husz - Timisoara, Romania. Lajos Rodek. Sign recognition ideas

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Traffic sign identification

Traffic SignIdentification

Team GProject 15


Team members
Team members

  • Lajos Rodek - Szeged, Hungary

  • Marcin Rogucki - Lodz, Poland

  • Mircea Nanu   - Timisoara, Romania

  •     Selman Kulac - Ankara, Turkey

  •     Zsolt Husz - Timisoara, Romania


Lajos rodek
Lajos Rodek

  • Sign recognition ideas

  • Sign library preparation

  • Presentation

  • Lots of laughing


Marcin rogucki
Marcin Rogucki

  • Sign recognition coding

  • Sign recognition ideas

  • Sign detection ideas

  • Presentation


Mircea nanu
Mircea Nanu

  • Sign detection ideas

  • Sign detection coding

  • Web page preparation

  • Moral support and jokes


Selman kulac
Selman Kulac

  • Gathering sign images

  • General ideas

  • Presentation


Zsolt husz
Zsolt Husz

  • Sign detection coding

  • Sign detection ideas

  • Picture acquisition

  • Many, many testing


Our goal
Our goal

  • Final goal: to detect and identify all traffic sign in arbitrary images


Assumptions
Assumptions

  • No human interaction

  • No preprocessing of the image

  • Flexible handling of images

  • Image is not rotated by more than 30 degrees

  • Images can contain any number of signs or no signs at all

  • Only daylight images are taken

  • At most ¼ of a sign may be covered

  • No background constrains / limitations


General program idea
General program idea

Program consists of two separated problems:

  • Detecting signs on the image

  • Recognizing detected regions of possible sign locations


Sign detection 1
Sign detection 1

Signs features:

  • Well defined colors with high saturation

  • They are rather homogenous

  • Sharp contours

  • Known basic shapes

  • Allowed colors:

    • Red, blue (dominant colors)

    • Yellow

    • Green (very rare)

    • White, black (found mostly inside of signs)


Sign detection 2
Sign detection 2

Main steps:

  • Edge detection (3 by 3 Sobel)

  • Converting image to HSV color space

  • Reducing number of colors

  • Segmentation relying on the color

  • Marking probable signs with boundary boxes

  • Joining adjacent regions

  • Removing background


Sign detection 3

Conversion

to grayscale

Sobel

Input

Region

extension

Conversion

toHSV

Color

detection

Border

extraction

Region

joining

Region

database

Output

Sign detection 3


Sign recognition 1
Sign recognition 1

Input:

  • Picture containing at most one sign (subrange of the original image) with eliminated background

  • Sign templates and names

    Output:

  • Sign name in case it is a traffic sign

  • Localization on the image


Sign recognition 2
Sign recognition 2

Tasks:

  • Detecting the shape of a sign

  • Finding corners if necessary

  • Transforming the shape (Perspective/rotation  Facing/upright)

  • Color unification

  • Comparison with templates


Sign recognition 3
Sign recognition 3

Detecting the shape:

  • Building a chain code

  • Computing angles between vectors

  • Checking number of the corners

  • Defining a shape

    (triangle,square,circle)


Sign recognition 4
Sign recognition 4

Finding corners:

  • “Charged particles” based approach

    Particles run away from each other and locate corners as furthest possible points in the figure


Sign recognition 5
Sign recognition 5

Transforming the sign:

  • Inverse texture mapping according to the corners and shape


Sign recognition 6
Sign recognition 6

Color unification:

  • Simplifying colors depending on similarity

    • Allowed colors:

      Red, green, blue, yellow, white, black, background (pink)

  • Computing a histogram


  • Sign recognition 7
    Sign recognition 7

    Comparison with a template:

    • Normalized histograms are compared resulting in a RMS measure

    • Raster pictures are compared pixel by pixel

    • Probability based decision





    Achievements
    Achievements

    • Everything works fine

    • Every team member is happy

    • Signs are detected and recognized correctly in most cases

    • All assumptions are met

    • Works even in unusual cases (e.g. night pictures)


    Future improvements
    Future improvements

    • Better reliability with fast motion blurring

    • More independency with illumination

    • Robustness on sign detection (fine-tuning the heuristically adopted constrains)

    • Better library templates

    • Speed-ups

    • Adaptation for a sequence of images



    References
    References

    • Intel, “Intel Image Processing Library, Reference Manual”, 2000, http://developer.intel.com

    • Intel, “Open Computer Vision Library, Reference Manual”,2001, http://developer.intel.com

    • D. A. Forsyth, J. Ponce, “Computer Vision: A Modern Approach”, Prentice Hall, 2003

    • George Stockman, Linda G. Shapiro, “Computer Vision”, Prentice Hall, 2001


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